March 2012 Sea Surface Temperature (SST) Anomaly Update – A New Look

I’ve added a new feature to the graphs of the monthly sea surface temperature updates on a trial basis, and it is the multi-model mean of the CMIP3 hindcasts/projections for sea surface temperatures, presenting them in comparisons to the observed data. The observed and modeled linear trends are also shown. This is done for the global, hemispheric and ocean basin sea surface temperature anomalies. As you will recall, CMIP3 is the climate model archive used by the IPCC for its 4thAssessment Report (AR4). I haven’t decided whether to include the model simulation data and trends in each monthly update or to include them only on a quarterly basis; that is, for the monthly updates in March, June, September, and December. I’m leaning toward providing them on a quarterly basis, not only because they’re a lot of extra work, but the model data also detracts from the data update itself. On the other hand, it would likely be good to provide the monthly reminder of just how poorly the models simulate global and regional sea surface temperatures.

The multi-model mean and linear trends of the CMIP3 model simulation data definitely make the graphs busier. Refer to the Global sea surface temperature anomaly graph. We added the smoothed data (13-month running-average filter) on a trial basis a few months ago, and readers requested that we keep the smoothed data. On some occasions, the trend lines may obscure the most recent changes in the dataset. Let me know whether we should include the additional climate model data in each monthly update or if you would prefer it on a quarterly basis.

(1) Global Sea Surface Temperature Anomalies

Monthly Change = -0.016 deg C

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NOTES ABOUT THE MODEL-OBSERVATION COMPARISONS

The model-observations comparisons serve as updates to two of my favorite posts: Satellite-Era Sea Surface Temperature Versus IPCC Hindcast/ProjectionsPart 1 and Part 2. Refer to those posts for the discussions of the monumental differences between the models and observations. They are also presented in my book, in Section 8.

A couple of notes: The multi-model mean data are not expected to present the year-to-year variations in sea surface temperature associated with the El Niño-Southern Oscillation (ENSO). Some of the models simulate ENSO; others don’t. The models that do attempt to simulate ENSO do a poor job of it. (This is documented in numerous peer-reviewed papers.) Each model produces ENSO events on its own schedule; that is, the modeled ENSO events do not reproduce the observed frequency, duration, and magnitude of El Niño and La Niña events. Since the multi-model mean presents the average of all of those out-of-synch ENSO signals, they are smoothed out. For this reason, we are only concerned with the disparity in the modeled and observed trends.

And as shown above, the difference between the linear trends on a global basis is quite large. The model simulations hindcast/project a global sea surface temperature anomaly warming rate that is about 80% higher than the observed rate. Depending on the subset, the models perform better and worse. For example, the model-simulated rate of warming for Northern Hemisphere sea surface temperature anomalies is only about 24% higher than observed, while in the Southern Hemisphere, the models say the sea surface temperatures should be warming at a rate that is more than 2.5 times faster than the observed rate.

Part 1 and Part 2 of Satellite-Era Sea Surface Temperature Versus IPCC Hindcast/Projections and my bookalso illustrate the differences in observed and modeled trends on a zonal mean basis. See example below for the global oceans, which shows the trends from November 1981 to February 2011. In my view, this is the greatest failing of the models. For the last 30 years, the models show the global oceans warming faster in the tropics than at mid latitudes and faster at mid latitudes than at the poles. And it’s very obvious that the global oceans have not warmed in that fashion over the past 30+ years. The warming of the global oceans since November 1981 is actually greatest toward the high latitudes of the Northern Hemisphere, while the oceans south of about 50S have cooled. Note also that there has been comparatively little warming at the equator.

(16) Global Zonal Mean Model-Data Comparison – Trends

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Those zonal mean graphs will not be updated as part of these posts.

Keep in mind, the global oceans represent about 70% of the surface area of the globe, and the climate models show no skill at being able to simulate the warming of their surface. As discussed under the heading of THE EAST PACIFIC VERSUS THE REST OF THE WORLD in this post, global sea surface temperatures have warmed over the past 30+ years in response to ENSO events, not anthropogenic greenhouse gases. This was presented and discussed in detail in my book titled If the IPCC was Selling Manmade Global Warming as a Product, Would the FTC Stop their Deceptive Ads?and in a good number of posts at my blog.

When CMIP5-based sea surface temperature data is available through the KNMI Climate Explorer, I will provide a post about it, to serve as a preview of the IPCC’s upcoming 5thAssessment Report.

Last, the differences between models and observations are not discussed throughout the rest of the post. The remainder is the normal monthly sea surface temperature update. Feel free, however, to comment on the disparity between the models and the observations.

MONTHLY SST ANOMALY MAP

The following is a Global map of Reynolds OI.v2 Sea Surface Temperature (SST) anomalies for March 2012 downloaded from the NOMADS website. The contour level is set at 0.5 deg C, and white is set at zero.

March 2012 Sea Surface Temperature (SST) Anomalies Map

(Global SST Anomaly = +0.106 deg C)

MONTHLY OVERVIEW

The Monthly NINO3.4 SST Anomaly continued their climb toward zero in March 2012, rising about +0.196 deg C, to -0.467 deg C, which is just above the -0.5 deg C threshold of a La Niña event.

(2) NINO3.4 Sea Surface Temperature Anomalies

(5S-5N, 170W-120W)

Monthly Change = +0.196 deg C

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On the other hand, Global Sea Surface Temperature anomalies, as shown at the opening of the post, cooled slightly, approximately -0.016 deg C. Both hemispheres cooled this month, with the greater drop taking place in the Northern Hemisphere. The monthly Global Sea Surface Temperature anomalies are presently at +0.106 deg C.

THE EAST PACIFIC VERSUS THE REST OF THE WORLD

Note: I have not included the model data for these two subsets. They would detract from the discussions of them.

The East Pacific and the Rest-Of-The-World (Atlantic-Indian-West Pacific) datasets were first discussed in the post Sea Surface Temperature Anomalies – East Pacific Versus The Rest Of The World.Both datasets have been adjusted for the impacts of volcanic aerosols. The global oceans were divided into these two subsets to illustrate two facts. First, the linear trend of the volcano-adjusted East Pacific (90S-90N, 180-80W) Sea Surface Temperature anomalies since the start of the Reynolds OI.v2 dataset is basically flat. The East Pacific linear trend varies with each monthly update. But it won’t vary significantly between El Niño and La Niña events.

And second, the volcano-adjusted Sea Surface Temperature anomalies for the Rest of the World (90S-90N, 80W-180) rise in very clear steps, and those rises are clearly in response to the significant 1986/87/88 and 1997/98 El Niño/La Niña events. It also appears as though the Sea Surface Temperature anomalies of this dataset are making another upward shift in response to the more recent 2009/10 ENSO event. For those who are interested in the actual trends of the Sea Surface Temperature anomalies between the 1986/87/88 and 1997/98 El Niño events and between the 1997/98 and 2009/10 El Niño events refer to Figure 4 in Does The Sea Surface Temperature Record Support The Hypothesis Of Anthropogenic Global Warming? I further described (at an introductory level) the ENSO-related processes that cause these upward steps in the post ENSO Indices Do Not Represent The Process Of ENSO Or Its Impact On Global Temperature.

I also went into greater detail to describe the ENSO-related processes that cause those upward shifts in Section 6 of my book. Section 6 takes the reader from a very basic description of the El Niño-Southern Oscillation (ENSO) through to how certain parts of the global oceans warm in response to El Niño AND La Niña events. Refer to the Table of Contents included in the .pdf file here.

(4) Volcano-Adjusted Sea Surface Temperature Anomalies For The Rest of the World

(90S-90N, 80W-180)

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The periods used for the average Rest-Of-The-World Sea Surface Temperature anomalies between the significant El Niño events of 1982/83, 1986/87/88, 1997/98, and 2009/10 are determined as follows. Using the NOAA Oceanic Nino Index(ONI) for the official months of those El Niño events, I shifted (lagged) those El Niño periods by six months to accommodate the lag between NINO3.4 SST anomalies and the response of the Rest-Of-The-World Sea Surface Temperature anomalies, then deleted the Rest-Of-The-World data that corresponds to those significant El Niño events. I then averaged the Rest-Of-The-World SST anomalies between those El Niño-related gaps.

Of course, something could shift. Will the upward ratcheting continue when the Atlantic Multidecadal Oscillation (AMO) decides to turn around and start its decline? The upward steps would not continue in the North Atlantic, but would the AMO impact the upward steps in other portions of the globe? For more information about the Atlantic Multidecadal Oscillation, refer to the post An Introduction To ENSO, AMO, and PDO — Part 2.

The Sea Surface Temperature anomalies of the East Pacific Ocean, or approximately 33% of the surface area of the global oceans, have decreased slightly since 1982 based on the linear trend. And between upward shifts, the Sea Surface Temperature anomalies for the rest of the world (67% of the global ocean surface area) remain relatively flat. Anthropogenic forcings are said to be responsible for most of the rise in global surface temperatures over this period, but the Sea Surface Temperature anomaly graphs of those two areas prompt a two-part question: Since 1982, what anthropogenic global warming processes would overlook the Sea Surface Temperatures of 33% of the global oceans and have an impact on the other 67% but only during the months of the significant El Niño events of 1986/87/88, 1997/98 and 2009/10?

NOTE ABOUT THE DATA

The MONTHLY graphs illustrate raw monthly OI.v2 SEA SURFACE TEMPERATURE anomaly data from November 1981 to March 2012, as it is presented by the NOAA NOMADS website linked at the end of the post. I’ve added the 13-month running-average filter to smooth out the seasonal variations.

Note: I discussed the (now apparently temporary) upward shift in the South Atlantic Sea Surface Temperature anomalies in the post The 2009/10 Warming Of The South Atlantic. It looks as though the South Atlantic sea surface temperature anomalies MAYreturn to the level they were at before that surge, and where they had been since the late 1980s. We’ll have to see where things settle.

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(9) North Pacific Sea Surface Temperature (SST) Anomalies

(0 to 65N, 100E to 90W)

Monthly Change = -0.031 Deg C

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(10) South Pacific Sea Surface Temperature (SST) Anomalies

(0 to 60S, 120E to 70W)

Monthly Change = -0.004 deg C

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(11) Indian Ocean Sea Surface Temperature (SST) Anomalies

(60S to 30N, 20E to 120E)

Monthly Change = -0.116 deg C

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(12) Arctic Ocean Sea Surface Temperature (SST) Anomalies

(65N to 90N)

Monthly Change = -0.031 deg C

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(13) Southern Ocean Sea Surface Temperature (SST) Anomalies

(90S-60S)

Monthly Change = +0.140 deg C

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WEEKLY SEA SURFACE TEMPERATURE ANOMALIES

The weekly NINO3.4 Sea Surface Temperature (SST) Anomalies have risen well above the threshold of a La Niña event. The NINO3.4 Sea Surface Temperature anomaly based on the week centered on April 4, 2012 is -0.262 deg C.

(14) Weekly NINO3.4 Sea Surface Temperature (SST) Anomalies

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The weekly global Sea Surface Temperature anomalies dropped a little over the past few weeks and are now at +0.116 deg C.

I don’t know why people insist on putting in running means. Unless the data has an extreme high-frequency component, we are perfectly capable of picking out the major trends without it. It usually just obscures the actual data.

Bob, I appreciate the updates, but TMI !!!! The information you put out gives me too much to think about in such a small place to think of such things!

That said, the North Pacific temps are interesting in relation to the sea ice we saw and are seeing. I don’t typically regard the arctic ice much. It is usually of little interest to me, but I think this year there is something worth watching, here. There may be some learning to be had.

Zac says: April 9, 2012 at 6:21 pm – So is it getting warmer or colder?

Zac – You took the words right out of my mouth. It appears to be yes and no at the same time. I guess that’s why I’m not a scientist.
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Jesse and Zac, obviously you guys are not sciency types…. elsewise you’d know the answer is ….. yes!!!

At first I was against showing the models, but it doesn’t obscure the data very much, and I think it’s important for people to see just how bad they are, in trend, variation, simulation of cyclical movements, etc. It’s clear to me that you need to have just about every parameter wrong to do so badly. I want my money back.

Look at the real data vs modeled for South Pacific. The real data shows huge swings, but the model can barely manage a squiggle as it goes along its high +trend.

Then look at Arctic and Southern ocean models. It looks like the model has a huge math error that makes any deviation from zero want to strongly correct to zero, and once close, deviate away again. The tops of the waves at the left of the chart and the bottoms of the waves on the right all want to converge to zero then get pushed away again. Like a pendulum with a magnet on the end that swings over an opposing magnet. Very strange. I would guess it is a very simple model with a very simple (but very significant) math error. It isn’t even close to modeling anything real (or it would have been compared to data and corrected by now).

What a great body of work and as is always the case, the truth will eventually out itself. The comparisons to the guess work of the IPCC is obvious. Once again Anthony, you demonstrate what science should really be all about. Just expose the facts without the influence of the AGW doctrine. will tell the truth every time. Well done..

Let me know whether we should include the additional climate model data in each monthly update or if you would prefer it on a quarterly basis.

As much fun as it is seeing how wildly the actual measurements differ from those “highly accurate” models’ predictions every month, I think quarterly updates will give you more time for the truly fun stuff.

Like items showing how far out on the limb the Abominable Waarmis (sorry, Mr. Lovecraft) are willing to go before they saw it off on themselves…

i have tried repeatedly to post this on Tips & Notes, but cannot, so am hoping it’s ok to post here, as this is being reported in Russia, Singapore, around the US etc, (On the road to Rio?) as MIT researchers say, or Smithsonian says, etc:

4 April: Yahoo: AP: Next Great Depression? MIT study predicting ‘global economic collapse’ by 2030 still on track
A renowned Australian research scientist says a study from researchers at MIT claiming the world could suffer from a “global economic collapse” and “precipitous population decline” if people continue to consume the world’s resources at the current pace is still on track, nearly 40 years after it was first produced.
The Smithsonian Magazine writes that Australian physicist Graham Turner says “the world is on track for disaster” and that current research from Turner coincides with a famous, and in some quarters, infamous, academic report from 1972 entitled, “The Limits to Growth.” Turner’s research is not affiliated with MIT or The Club for Rome…
***However, the study said “unlimited economic growth” is still possible if world governments enact policies and invest in green technologies that help limit the expansion of our ecological footprint…
Turner says that perhaps the most startling find from the study is that the results of the computer scenarios were nearly identical to those predicted in similar computer scenarios used as the basis for “The Limits to Growth.”
“There is a very clear warning bell being rung here,” Turner said. “We are not on a sustainable trajectory.”…
***Correction: This post has been edited to reflect that MIT has not updated its research from the original 1972 study.

April 2012: Smithsonian Magazine: Mark Strauss: Looking Back on the Limits of Growth
Forty years after the release of the groundbreaking study, were the concerns about overpopulation and the environment correct?
However, the study also noted that unlimited economic growth was possible, if governments forged policies and invested in technologies to regulate the expansion of humanity’s ecological footprint. Prominent economists disagreed with the report’s methodology and conclusions. Yale’s Henry Wallich opposed active intervention, declaring that limiting economic growth too soon would be “consigning billions to permanent poverty.”
Turner compared real-world data from 1970 to 2000 with the business-as-usual scenario…

6 April: Popular Science, Australia: Rebecca Boyle: MIT Predicts That World Economy Will Collapse By 2030
Is this impossible to fix? No, according to both Turner and the original study. If governments enact stricter policies and technologies can be improved to reduce our environmental footprint, economic growth doesn’t have to become a market white dwarf, marching toward inevitable implosion…

At first I was against showing the models, but it doesn’t obscure the data very much, and I think it’s important for people to see just how bad they are, in trend, variation, simulation of cyclical movements, etc. It’s clear to me that you need to have just about every parameter wrong to do so badly. I want my money back.

Look at the real data vs modeled for South Pacific. The real data shows huge swings, but the model can barely manage a squiggle as it goes along its high +trend.

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as bob tried to point out SEVERAL models are averaged here. And the models each have a number of runs. The “wiggles” of individual runs will all be averaged away. the average of all runs or all models will never match the single realization of the real earth. They cant and should not.
That could only be accomplished by tuning models to observations. The key metrics are
1. getting the global trend correct
2. getting hemispherical trends right.
3. getting the frequency and amplitude of major cycles correct.
4. getting the land/ocean contrast correct.

wiggles wont match. cant match. Now, when you look at the metrics that matter the models still need improvement.

I like the “old look” without the ensemble means. Maybe a separate posting with the model ensembles but I like the ones you produced every month with just the monthly and weekly with the trends and the running mean.

The zonal mean – trend per decade graphic shows peaks at the latitudes where the bulk of the developed world’s cities are (including the major South American cities). In my view most of the post 1960s land surface temperature rise can be attributed to reduced developed world anthropogenic aerosols (=increased insolation). Can aerosols also be affecting SSTs?

Unless the model runs were created in 1998, they should not be aligned with the data at 1998. Align them at the start date (or the average of the first couple of years); this will give a much better idea of divergence of models and data.

Bob, what abour /lack of/ recent OHC recharge by La Nina? The latest afterNino flat period seems similar to the previous one. If your ENSO heat release/heat recharge is correct fior the warm AMO period, there should be some mechanism allowing for step-down period now for the cold one.. any ideas yet?

Graphs 5,6 and 13 are particularly telling. Clearly a lot of heat moved north across the equator during ‘global warming’. The weekly graphs at the end of the post indicate we are just over the top of the warming curve since 2004.

The models have totally and utterly failed. They don’t account for the interhemispheric heat transport which proves that the oceans garner and retain heat on long time scales, contrary to what we’ve been told by ‘experts’ in the literature and on this blog by Leif.

The exposure of the fairy stories from Australia and New Zealand regarding rising land temperatures in the midst of a cooling ocean demonstrate the lengths the climate establishment went to in order to maintain the illusion. Yes the air temperature rose in the south due to a preponderance of El Nino over La nina between 1976 and 2010, but when the relative heat capacities of the two fluids are considered, it’s plain that natural factors caused most of mild warming we had.

so the SST follows the ENSO cycle and, other than the parts of the Pacific, shows a increasing temperature trend over several decades.

Interpriting that as ‘step changes’ may not be accurate, a sineusoidal signal and a linear trend will create something that looks like a step sequence. It is also unclear why each La Nina event has been warmer than the past episode for around a century instead of cooling back to the previous level. The lack of cooling requires ewither an additional source of energy, or a change in the rate of energy loss.

There is of course one obvious and measured cause of slower cooling and a retention of extra energy after El Nino events that would prevent the following La Nina cooling back to previous levels.

izen says: “It is also unclear why each La Nina event has been warmer than the past episode for around a century instead of cooling back to the previous level.”

Why would a La Nina cool “back to the previous level”? A La Nina is not the opposite of an El Nino event. When a La Nina follows an El Nino, the La Nina recharges the heat released by the El Nino through an increase in downward shortwave radiation, and it also redistributes the warm water that was leftover from the El Nino. The links in that section explain and illustrate the processes that cause the upward shifts.

braddles says: “Unless the model runs were created in 1998, they should not be aligned with the data at 1998. Align them at the start date (or the average of the first couple of years); this will give a much better idea of divergence of models and data.”

The base years for anomalies are the NOAA standard for the Reynolds OI.v2 SST data, 1971 to 2000.

Bob, what abour /lack of/ recent OHC recharge by La Nina? The latest afterNino flat period seems similar to the previous one. If your ENSO heat release/heat recharge is correct fior the warm AMO period, there should be some mechanism allowing for step-down period now for the cold one.. any ideas yet?

As I’ve been saying for the last 4 years; now that the cloud amount is increasing again, the quiet sun won’t recharge the ocean heat content as much during la Nina, and further El Nino’s will deplete the OHC further. La Nina ‘recharged’ OHC while cloud fraction was reduced, but the party is over.

The solar activity/cloud link is obvious, whether due to Svensmark effect or Stephen Wilde’s latitudinal climate zone shifts. It was identified years ago by Nir Shaviv, who nailed it with his JGR paper. http://sciencebits.com/calorimeter

Juraj V. says: “If your ENSO heat release/heat recharge is correct fior the warm AMO period, there should be some mechanism allowing for step-down period now for the cold one.. any ideas yet?”

Would the ENSO discharge/recharge be impacted by the mode of the North Atlantic?

Let’s forget about the North Atlantic for a moment. The East Pacific SST anomalies haven’t risen in 30 years. Let’s assume it continues to be flat. That leaves us with the South Atlantic-Indian-West Pacific Oceans. The SST anomalies of that subset rose in response to the major El Niño events and then decayed over the following decades, until the next major El Niño.

Let’s assume there are no major El Niño events for a couple of decades, shouldn’t the SST anomalies of the South Atlantic-Indian-West Pacific slowly decline? If so, that would leave the North Atlantic with the positive trend, assuming it stays as a positive trend. But, eventually, the AMO will switch modes.

What is the expected impact of increased albedo on SSTs, and where is that impact expected? Given over 7% increase in albedo over the last fifteen years (largely from increased mean cloud cover) it should be starting to affect the ocean’s energy budget outside of (or rather, literally on top of) the usual ENSO-driven cycle. Do the models explicitly take this into account? The cooling for this large an increase “should” be order of 2 degrees K worldwide, but because the Earth is a self-organized system it might take decades longer for the full impact to be felt. However, I’d expect the ocean to be where it has the greatest impact in the long run, slowly reducing the heat content of the top few hundred meters of the seawater as vertical mixing occurs and heat lost is not replenished at he same rate.

Yup, there are a number of sea surface temperature datasets that include data before 1982:
HADSST2
HADSST3
HADISST
ERSST.v3b
Kaplan

I concentrate on the Reynolds OI.v2 because it is satellite based and has, basically, complete coverage, where the others do not before 1982. HADISST blends the non-satellite-based, pre-1982, data with the satellite-based data, post-1982, data. All of those sea surface temperature datasets are available through the KNMI Climate Explorer:

I am working on cutting my tables which now comprise of almost 40 weather stations ( 20 from each hemisphere) . I am doing the regressions (trends) from the beginning (37 yrs on average), then 32 years (1980), then 22 year (1990), then 12 yrs (2000), then 7 yrs (2005).
The cut off times were chosen more or less randomly ( I tried from the middle of a suncycle to the middle of the other – ???) unless any of you guys have better ideas?
Rgrds. Henry

Danger Will Robinson! The downward trend seems to be in line with the low pass filtered UAH. I pray it’s a sine wave or something like it, long term. It may not be that good. We may actually be in the ringing just prior to the state change (e.g. the end of the interglacial).

rgbatduke says:
April 10, 2012 at 5:30 amWhat is the expected impact of increased albedo on SSTs, and where is that impact expected? Given over 7% increase in albedo over the last fifteen years (largely from increased mean cloud cover) it should be starting to affect the ocean’s energy budget outside of (or rather, literally on top of) the usual ENSO-driven cycle. Do the models explicitly take this into account? The cooling for this large an increase “should” be order of 2 degrees K worldwide, but because the Earth is a self-organized system it might take decades longer for the full impact to be felt. However, I’d expect the ocean to be where it has the greatest impact in the long run, slowly reducing the heat content of the top few hundred meters of the seawater as vertical mixing occurs and heat lost is not replenished at he same rate.

rgb

Continuing our discussion on thing nonlinear etc … If (as is likely) the earth is indeed a self organised nonlinear pattern system, then the result from such a change in albedo might be unpredictable, and could be faster as well as slower than expected. And rather than driving the system the albedo change could be symptomatic of a coordinated system transition. A system showing nonlinear-nonequilibrium dynamics can be characterised as following a “power law” as seen in a log-log plot in its fluctuations with time. This means if you were to plot the change in global temperatures over say 10 or 100 year intervals, over a much longer period, against the frequency of each change magnitude, and plot both as natural log, then at least a significant portion of the resultant log-log plot would form a straight line. The gradient of this line would represent the fractal dimension of the system. I calculated this once for the Vostok ice core records but dont have the data to hand.

This means that the internal chaotic dynamics of the system spontaneously generate variations, that most of these are small, but occasionaly they are large, and very occasionally, very large indeed (e.g. glacial-interglacial).

Why is it customary to use a 13 month moving average on climate charts? A 12 month MA would completely suppress the 12 month annual cycle; a 13 month MA does not. Is there a reason why you want the annual cycle to appear in the average?

This means if you were to plot the change in global temperatures over say 10 or 100 year intervals, over a much longer period, against the frequency of each change magnitude, and plot both as natural log, then at least a significant portion of the resultant log-log plot would form a straight line. The gradient of this line would represent the fractal dimension of the system. I calculated this once for the Vostok ice core records but don’t have the data to hand.

Sure, but if one looks at:

(Lisiecki and Raymo, 2005) and take it at face value, there are several features that argue against the ice age being due to this sort of fractal variability.

a) The climate was cooling slowly from 5 mya up to 3 mya (from a baseline that appears about 2 K warmer than current temperatures and that I cannot help but believe is the absolute upper bound of any anthropogenic or natural warming short of a complete rearrangement of the continents or alteration in the dynamics of the solar system including the sun itself). In this state the climate oscillated by roughly 1 K up or down around a running but fairly smooth trend. The oscillation here is likely chaotic noise of the sort you describe, but not unbounded — there is clear negative feedback stabilizing it on a timescale of a few tens of thousands of years. On this graph oscillations of decades or centuries are irrelevant and invisible — secular variation is all on the time scale of millennia.

b) Between 3.3 and 2.8 mya, the character of the warm phase altered. The cooling accelerated and the first precursor cold spikes occurred, but the climate returned to what was still arguably a slowly depressing warm phase afterwards.

c) Between 1 mya and 2.5 mya, the earth cooled and average of some 6 K, or 1 K every 250,000 years, while oscillating sharply between a degree or two warmer and a degree or two colder. “Sharply” is with a period of some 40 ky, but there is plenty of (again, probably chaotic) noise and excursion. A new (and highly interesting) feature emerges. At the beginning, the small oscillations did not suffice to return the planet to its initial warm-phase baseline vicinity. However, the (mean) colder that it grew, the greater the amplitude of the oscillations, and starting some 1.75 mya the oscillations at least sometimes managed to return to warm phase conditions, but always for a heartbreakingly brief period of geologic time. On this scale they appear as sharp spikes, but those spikes are a few thousand years in duration.

d) Around 1 mya the system appears to stabilize around a consistent mean temperature some 5.5 to 6 K cooler than the present (and the now-ancient warm phase). The character of the oscillations radically shifts, altering both period and amplitude. Upward swings are around 6 K, highly transient excursions back to a warm phase. Downward swings are some 4 K, with the coldest times some 10K cooler than the present (and “dangerously cool” from the point of view of CO_2 absorption into the ocean and its risk to land-based plant life — CO_2 levels dropped to partial pressures only a bit larger than that needed to sustain plant life and hence land based animal life). Although there is a lot of noise, over the last 600,000 ya, the warm phase excursions have had a crude periodicity of 100 ky or so with fairly well separated and distinct warm peaks, well-defined “interglacials” as it were.

If one looks at e.g. the Vostok cores to try to get a higher resolution picture of this latter interval, one sees that the Holocene is not (yet) the warmest interglacial — its peak temperatures (which probably occurred some 10 kya near the beginning) were a degree or so cooler than at least one or two of the other interglacial peaks in this interval. It is already one of the longest interglacials — there is one that was longer but it was also somewhat cooler.

Looking at this, it is highly implausible that the data from 3 mya to the present can be adequately explained just by chaotic oscillation in a system with otherwise steady drivers. There is clear evidence of “events” or discrete changes of some of the important drivers, and equally clear evidence of quasi-resonance with some external driver modulation (41 ky and 100 kya periodicity). IMO none of those modulations match up particularly well with known or understandable periods of orbital dynamics, although some complicated pattern of double or triple resonances being fed back through a chaotic internal dynamics make this hypothesis still tenable if not terribly consistent with the actual data as far as we can at this time understand it.

Here are things that I think function as confounding factors for most simple explanations. It is difficult to explain the gradual cooling precursor to the more rapid drop. If the opening of the Panama Seaway altered oceanic circulation abruptly that is fine, but how did the effect precede this nearly discrete cause? Also, where are Milankovitch cycle modulations at this time? Presumably they were occurring, but they had no meaningful effect on the global climate — the fluctuations here are basically pure noise with no particular orbital resonance signal. The smooth depression of the mean temperature (smoothed over perhaps 50 ky) has nothing whatsoever of “chaos” about it. This is clearly a secular motion of a chaotic attractor (perhaps) brought about by variation of some external driver that is smoothly varying on a time scale of tens of thousands of years over 1.5 million years! Finally, why, when this driver finally stabilized in a cold phase state with extremely large and asymmetric thermal excursions, did a 100 ky cycle of highly transient but fairly consistent warm phase excursions emerge?

There is an interesting way to plot the phase space of this system that I may attempt. If one puts time on (say) the x axis, temperature on the y axis and an effective potential on the z axis that is characterized by the quadratic return rate to a locally stable equilibrium, then the minimum of the potential occurs at the location of the attractor and width provides a measure of its local stability. The surface thus rendered would be sharp and narrow in warm phase a) above and then would widen as it descends b), at first symmetrically b), c) and then asymmetrically d), with larger warm excursions than cold ones.

Such a curve would be a first step in trying to deduce the actual Poincare cycle history that included the secular motion of the primary attractor in some reasonable number of dimensions, trying very hard to do a systematic decomposition of those dimensions from most to least important and at least trying to associate some explanation with the primary motion of the attractor.

I’m perfectly happy with albedo modulation being the core feedback responsible for the oscillations about the mean, although albedo modulation is itself multifactorial and not terribly well understood. However, it is not terribly plausible as a cause of the movement of the primary attractor, the stable point around which albedo modulation swings the local climate. The time scale of the secular motion is too long! What is the memory of this non-Markovian system that would cause smooth variation of the mean temperature on million year timescales when at most a few tens of thousands of years appears sufficient to reset any possible clock, including the ocean, unless there were a driver that varied persistently across those timescales?

Again, this is the sort of thing that needs to be done thoroughly — and I do mean thoroughly, to the point of general acceptance of the explanation — before one screws around with baseless assertions of catastrophic warming. The latter are basically asserting that the Earth has a still warmer warm phase that is locally stable under the planet’s current geological and solar dynamic drivers, and that CO_2 alone is sufficient to push the climate into this new stable driver. Yet the direct evidence of the Earth’s climate history at the very least fails to provide the faintest hint of support for this hypothesis, if it doesn’t quite outright refute it. If anything, the Earth is pulled as far out in the warming direction as it ever goes, or has gone, for millions of years with a much more stable thermal state that is some 5-6K cooler just waiting to happen.

Until we fully understand what the Earth’s temperature should be, in the sense that we can completely understand if not predict this entire 5 my curve in terms of its most important parametric drivers and the actual Poincare helix the Earth’s temperature traces out in time, we cannot possibly resolve the powerful and dominant long term signal from the noise and hope to detect the non-noise signal of “CO_2 variation” from everything else, let alone predict future behavior.

Anyone who examines this 5 my history should be very, very afraid. There is indeed a “catastrophe” awaiting the human race, one that we haven’t the slightest ability to influence. That is the cold catastrophe that is quite inevitable, and that could occur quite literally at any time. We don’t know why it will happen or when it will happen when it happens. We don’t understand why the Earth turned cold in the first place and why it has warm phase excursions now. Yet we are “confidently” predicting warming unprecedented over the last five million years! Piffle!

If it happened, and if it managed to stabilize the entire planet in warm phase for a few hundred thousand years or longer, that would be simply awesome. Warm phase is fecund and happy, with the entire Earth a hotbed of life. Cold phase is dangerous — Ragnarok indeed — with frost giants stalking the landscape and the carbon dioxide that land life itself depends on being sucked out of the air by the relentless, cold, sea. Drop it below 180 ppm and plant life vanishes from the poles to the equator (unless it manages to evolve to where it can survive at still lower partial pressures). Things that live on plants starve. Land life dies and the Earth starts over. And yeah, the croplands of today are covered once again by massive glaciers long before this happens, so the human race necessarily shrinks back to a much smaller surviving core, probably in the most violent of ways.

Sure, not in my lifetime. Probably not in the lifetime of my children or grandchildren. But the whole point of science is to understand all of this and ultimately, to be able to predict and perhaps control it.

My plan for stopping Ragnarok is two orbiting MW satellite rings, one about 20K up in polar orbit, the other 25K up in equatorial, with swivelling emitters, cooking the ice 24/7! With a few receiver grids here and there to tap off as much electricity as desired.

I have a real problem accepting those kind of graphs (1), when data scatter is greater than the trend and the phenomenon is probably cyclic (thus straight line fits are a wrong approach).
That graph only covers the period from close to when climate temperature was at its last low to the present.
If you look only at the period from the last high to present a straight line fit would show no increase or perhaps slight decrease.
A minimum period would be 100 years, to completely span the possible 60-year cycle (1930s warm period to 1990s warm period and a margin each side of that).
Even that is dodgy, because there may be several cyclic functions of different periodicity, the combination of which may vary in strength and timing (note that ENSO does).
It does seem to disprove IPCC predictions.

Keith Sketchley says: “I have a real problem accepting those kind of graphs (1), when data scatter is greater than the trend and the phenomenon is probably cyclic (thus straight line fits are a wrong approach).
“That graph only covers the period from close to when climate temperature was at its last low to the present.”

This is a satellite-based sea surface temperature dataset that starts in November 1981. That’s as far back in that it goes.